The paper introduces MCIR-M, a global feature‑importance method that accounts for feature dependence by conditioning each feature on its strongly dependent neighbours and computing a normalized Mutual Correlation Impact Ratio. MCIR-M yields a population score between 0 and 1, reaching zero when a feature is exactly conditionally redundant, and can be estimated with only a fraction of the data. Experiments on synthetic redundancy setups and the UCI HAR benchmark show that MCIR-M produces more stable rankings under near‑duplicate predictors compared to existing methods such as SHAP, SAGE, HSIC, and MI‑based scores.
By Poushali Sengupta, Sabita Maharjan, Frank Eliassen, Shashi Raj Pandey, Yan Zhang
arXiv:2610.35740v1 Announce Type: cross
Abstract: Computing local-level variable importance (VI) is critical for understanding machine learning models. Local permutation-based VI metrics offer a simp...
By Yixiao Wang, Srikar Katta, Jon Donnelly, Emanuele Borgonovo, Cynthia Rudin
Computing local-level variable importance (VI) is critical for understanding machine learning models. Local permutation-based VI metrics offer a simple yet powerful approach, measuring how much a mode...
arXiv:2609.36396v1 Announce Type: cross
Abstract: As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical cha...
By Yinan Cheng, Lili Zheng
arXiv:2511.15371v3 Announce Type: replace
Abstract: Assessing the importance of individual features in Machine Learning is critical to understand the model's decision-making process. While numerous m...
By Eddie Conti, \'Alvaro Parafita, Axel Brando
arXiv:2608.27704v1 Announce Type: new
Abstract: When machine learning classifiers are retrained, inputs correctly classified by the previous model version may be misclassified by the updated version,...
By Madhusudan Srinivasan, Namith Nishal Raphae
arXiv:2604. 15107v2 Announce Type: replace-cross Abstract: Shapley values provide a flexible framework for attributing feature contributions to model predictions, but they are not naturally suited for feature selection: a feature may receive a positive attribution even when it is redundant given the remaining variables.
By Chenghui Zheng, Garvesh Raskutti
arXiv:2510.12734v2 Announce Type: replace
Abstract: Variable importance (VI) methods are often used for hypothesis generation, feature selection, and scientific validation. In the standard VI pipelin...
By Jon Donnelly, Srikar Katta, Emanuele Borgonovo, Cynthia Rudin
arXiv:2606. 01111v1 Announce Type: new Abstract: Modern industrial recommender systems rely on thousands of heterogeneous features -- ranging from low-dimensional scalars (e.
By Yihong Huang, Chen Chu, Fei Chen, Yu Lin, Ruiduan Li, Zhihao Li
arXiv:2603. 14894v3 Announce Type: replace-cross Abstract: Trust and ethical concerns due to the widespread deployment of opaque machine learning (ML) models motivating the need for reliable model explanations.
By Sumedha Chugh, Ranjitha Prasad, Nazreen Shah
arXiv:2303. 08777v3 Announce Type: replace-cross Abstract: Cross-validation is one of the most widely used tools for risk estimation and model selection in statistics and machine learning, yet its theoretical properties when embedded in a learning procedure remain insufficiently understood.
By Diego Marcondes, Cl\'audia Peixoto
The paper introduces Adaptive Derivative-Ordered Random Explanation (ADORE), a unified framework that uses first- and second-order derivatives to capture nonlinear feature interactions and feature-sample dynamics. ADORE combines global feature importance with local sample contributions, quantifying both magnitude and direction of feature impact while identifying critical samples. It achieves computational efficiency via randomized SVD and dynamic sparsity detection, outperforming LIME and SHAP across tabular, text, and image data, and is released as an open-source Python package on GitHub.
By Lemen Chao, Ming Lei, Anran Fanga